TensorFlow High-Level APIs: Models in a Box (TensorFlow Dev Summit 2017)
Why Stack Overflow recommends it
Stack Overflow users link it as the authoritative source on the core versus contrib distinction and on why Estimator-related deprecation warnings appear.
Recommended by 2 people across 2 different months, 2017 to 2017.
What it is
A 17-minute talk from the TensorFlow Dev Summit 2017 in which Martin Wicke of Google introduces TensorFlow's high-level model-building APIs: Layers, Estimators and Canned Estimators. He also lays out the roadmap for moving these from tf.contrib into core TensorFlow.
What people said
Recommends Bryon · 2017-03 · Stack Overflow · on “Undeprecating tensorflow”
This portion of the Github tree [LINK] is under active development. I expect this warning message to go away once the Estimator class is moved into tf.core which is schedule for version r1.1. I found the 2017 TensorFlow Dev Summit video [this video] by Martin Wicke to be very informative on the future plans of high level TensorFlow.
Recommends Maxim · 2017-11 · Stack Overflow · on “What is the difference between tf.estimator.Estimator and tf.contrib.learn.Estimator in TensorFlow”
To add to Christoph's answer. The distinction between these packages has been specifically mentioned at Tensorflow Dev Summit 2017 by Martin Wicke [this video] : The distinction between core and contrib is really in core things don't change. Things are backward compatible until release 2.0, and nobody's thinking about that right now. If you have something in core, it's stable, you should use it. If you have something in contrib, the API may change and depending on your needs you may or may not want to use it. So you can think of tf.contrib package as "experimental" or "early preview". For classes that are already in both tf.estimator and tf.contrib , you should definitely use tf.estimator version
Cites Bryon · 2017-03 · Stack Overflow · on “tf Iris - how to avoid warning message on deprecated estimator”
It looks like this warning is being generated by the TensorFlow library tf.contrib.learn.estimator.py . This warning appears to have been added on 2016-12-01 when the decision was made to drop support for x= and y= in favor of the input_fn= parameter to BaseEstimator . Based on the comments made by Martin Wicke during the TensorFlow Dev Summit [this video] , all the procedures in tf.contrib are subject to change up until the point they are moved into TensorFlow Core. He also stated that the previous interface would be supported by SKEstimator. The Google team has done a great job of keeping the tutorials up todate. The stated plan is to move Estimators into TensorFlow Core in the next few months.
Cites Salvador Dali · 2017-07 · Stack Overflow · on “TensorFlow, why was python the chosen language?”
TF is not written in python. It is written in C++ (and uses high-performant numerical libraries [LINK] and CUDA [LINK] code) and you can check this by looking at their github [LINK] . So the core is written not in python [this video] but TF provide an interface to many other languages ( python, C++, Java, Go [LINK] ) [LINK] If you come from a data analysis world, you can think about it like numpy (not written in python, but provides an interface to Python) or if you are a web-developer - think about it as a database (PostgreSQL, MySQL, which can be invoked from Java, Python, PHP) Python frontend (the language in which people write models in TF) is the most popular due to many [LINK] reasons [LINK